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Record W4414063403 · doi:10.1080/00083968.2025.2524338

The social costs and injustices of militarized approaches to illegal artisanal small-scale mining governance in Ghana

2025· article· en· W4414063403 on OpenAlexaffvenue
Phil Faanu, Griselda Asamoah-Gyadu

Bibliographic record

VenueCanadian Journal of African Studies / Revue canadienne des études africaines · 2025
Typearticle
Languageen
FieldEngineering
TopicMining and Resource Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCorporate governanceGovernment (linguistics)Good governancePovertyPopulation

Abstract

fetched live from OpenAlex

Illegal artisanal small-scale mining (ASM) in Ghana is a livelihood-supporting economic activity with associated environmental impacts, prompting government intervention through a joint military–police task force. This response has involved security personnel raiding illegal mining operations. However, the actions of the task force have resulted in significant social injustices and costs within affected communities. This paper contributes to the ASM literature by examining these injustices through a social justice framework, highlighting the economic, emotional and physical toll on miners and their communities. We demonstrate how state-led interventions aimed at curbing illegal mining have created social and human injustices, including the destruction of property and equipment, and the loss of life. In light of these findings, we join ongoing calls for the formalization and regularization of the ASM sector, emphasizing the need for more inclusive, humane and sustainable governance approaches.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.008
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.218
Teacher spread0.180 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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